[1]} argues that such generalisation
to novel features is not possible for connectionist
models due to what he calls training independence.
Roughly, he assumes that the weights within these
networks are learned independently, with the consequence
that no updates are made to the connections
attached to any unit which is inactive during training.
Although they are critical of the
conclusions drawn by [2]}
from the rule learning experiments,
[3]} nonetheless affirm
that generalisation in neural networks
depends on overlapping patterns of activity
somewhere in the network.
Furthermore, in a review of the literature
surrounding those experiments, [4]}
also assume that generalisation will
depend on the overlap between vectors
in training and test.
